What is Llm Mcp Travel Orchestrator

The LLM-MCP Travel Orchestrator is a sophisticated multi-agent travel accommodation system that utilizes OpenAI’s GPT-4o-mini, LangChain, and the Multi-Agent Collaboration Protocol (MCP) for intelligent property search and recommendations. The system orchestrates multiple AI agents to handle tasks such as query parsing, filtering, summarization, and providing real-time accommodation options.

Use cases

The system allows users to conduct property searches based on location, filter accommodations by amenities, specify price ranges, and receive dynamic recommendations. It enables context-aware conversations with personalized suggestions and supports multi-agent collaboration for complex queries.

How to use

To use the system, clone the repository, set up a Python virtual environment, install required dependencies, and configure your OpenAI API key in a .env file. Run the application using Streamlit, and access it through a web browser at the specified local address to interact with the chatbot for accommodation queries.

Key features

Key features include chain-of-thought reasoning through LangChain, real-time property data retrieval via MCP, context-aware conversation management, intelligent response generation, dynamic filtering options, and personalized accommodation recommendations based on user input.

Where to use

This system can be effectively used in travel agencies, online travel platforms, and hospitality services that require efficient accommodation searches and real-time recommendations, enhancing user experiences in the travel planning process.

🤖 LLM-MCP Travel Orchestrator

A sophisticated multi-agent travel accommodation system leveraging OpenAI’s GPT-4o-mini, LangChain, and the Model Context Protocol (MCP) to provide intelligent property search and recommendations. This system orchestrates multiple AI agents for query parsing, filtering, summarization, and real-time accommodation recommendations.

🧠 Technical Architecture

Multi-Agent System

• LLM Orchestration
  • GPT-4o-mini powered natural language understanding
  • Multi-agent collaboration for complex tasks
  • Context-aware conversation management
  • Intelligent response generation
• LangChain Integration
  • Chain-of-thought reasoning
  • Tool-based execution
  • Memory management
  • Response formatting
• MCP Server Integration
  • Real-time property data access
  • Asynchronous communication
  • Robust error handling
  • Efficient data retrieval

Core Components

1. LLM Agent Layer
2. LangChain Integration Layer
3. MCP Integration Layer
4. User Interface Layer

🚀 Getting Started

Prerequisites

• Python 3.11 or higher
• Node.js and npm
• OpenAI API key (Get one here)

Installation

1. Clone the Repository

git clone https://github.com/ANUVIK2401/LLM-MCP-Travel-Orchestrator.git
cd LLM-MCP-Travel-Orchestrator

2. Set Up Virtual Environment

python -m venv venv
# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
.\venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt
npm install -g @openbnb/mcp-server-airbnb

4. Configure Environment
   Create a .env file in the project root and add your OpenAI API key:

OPENAI_API_KEY=your_api_key_here

⚠️ Important: Never commit your .env file or share your API key. The .env file is already in .gitignore for security.

Running the Application

streamlit run chatbot.py

Then open your browser and navigate to: http://localhost:8501

💡 Usage Guide

• Property search by location
• Amenity-based filtering
• Price range specifications
• Location-based recommendations
• Multi-agent collaboration
• Context-aware conversations
• Dynamic filtering options
• Personalized recommendations

📸 Screenshots

Main Chatbot Interface	Property Search Results

Multi-Agent Collaboration	Real-Time Recommendations

🗂️ Project Structure

LLM-MCP-Travel-Orchestrator/
├── assets/
│   └── images/
├── chatbot.py
├── airbnb_use.py
├── airbnb_mcp.json
├── requirements.txt
├── pyproject.toml
├── pytest.ini
├── LICENSE
├── .gitignore
├── docs/
├── mcp_use/
│   ├── agents/
│   ├── connectors/
│   ├── task_managers/
│   ├── client.py
│   ├── config.py
│   ├── logging.py
│   ├── session.py
│   └── __init__.py
├── tests/
│   ├── conftest.py
│   └── unit/
│       ├── test_client.py
│       ├── test_config.py
│       ├── test_http_connector.py
│       ├── test_logging.py
│       ├── test_session.py
│       └── test_stdio_connector.py
└── venv/

📚 Documentation

• See the docs/ directory for detailed guides, quickstart, and API reference.
• Example: docs/introduction.mdx, docs/quickstart.mdx

🛠️ Development

1. Fork the repository
2. Create a feature branch
3. Set up your development environment
4. Make your changes
5. Test thoroughly (see tests/ directory)
6. Submit a pull request

Key Dependencies

• streamlit==1.32.0
• python-dotenv==1.0.0
• mcp-use==1.1.5
• langchain-openai>=0.0.5
• langchain-community>=0.0.34
• langchain>=0.1.16

🔒 Security Considerations

• Keep your API keys secure
• Never commit sensitive information
• Use environment variables
• Regular dependency updates
• Follow security best practices

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• openbnb-org/mcp-server-airbnb for the MCP server
• OpenAI for the GPT models
• LangChain for the agent framework
• Streamlit for the web framework

📞 Support

For support:

1. Check the Issues page
2. Create a new issue if your problem isn’t already listed
3. Contact the maintainers for urgent issues

Made with ❤️ by [Anuvik Thota]